Wyniki wyszukiwania - BazTech

1

Brain stem modeling at a system level - chances and limitations

Mikolajewski D., Duch W.

Bio-Algorithms and Med-Systems

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2018

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Vol. 14, no. 2

art. no. 20180015

EN

The topic of brain stem computational simulation still seems understudied in contemporary scientific literature. Current advances in neuroscience leave the brain stem as one of the least known parts of the human central nervous system. Brain stem lesions are particularly damaging to the most important physiological functions. Advances in brain stem modeling may influence important issues within the core of neurology, neurophysiology, neurosurgery, and neurorehabilitation. Direct results may include both development of knowledge and optimization and objectivization of clinical practice in the aforementioned medical areas. Despite these needs, progress in the area of computational brain stem models seems to be too slow. The aims of this paper are both to recognize the strongest limitations in the area of computational brain stem simulations and to assess the extent to which current opportunities may be exploited. Despite limitations, the emerging view of the brain stem provided by its computational models enables a wide repertoire of functions, including core dynamic behavior.

2

Fuzzy-based computational simulations of brain functions - preliminary concept

Prokopowicz P., Mikołajewski D.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 3

99-104

EN

Research on the computational models of the brain constitutes an important part of the current challenges within computational neuroscience. The current results are not satisfying. Despite the continuous efforts of scientists and clinicians, it is hard to fully explain all the mechanisms of a brain function. Computational models of the brain based on fuzzy logic, including ordered fuzzy numbers, may constitute another breakthrough in the aforementioned area, offering a completing position to the current state of the art. The aim of this paper is to assess the extent to which possible opportunities concerning computational brain models based on fuzzy logic techniques may be exploited both in the area of theoretical and experimental computational neuroscience and in clinical applications, including our own concept. The proposed approach can open a family of novel methods for a more effective and (neuro)biologically reliable brain simulation based on fuzzy logic techniques useful in both basic sciences and applied sciences.

3

Simple cyclic movements as a distinct autism feature - computational approach

Dobosz K., Mikołajewski D., Wójcik G. M., Duch W.

Computer Science

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2013

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Vol. 14 (3)

475--489

EN

A diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders (ASD). Each year, the percentage of children diagnosed with ASD is growing. One common diagnostic feature in individuals with ASD is the tendency to exhibit atypical simple cyclic movements.The motor brain activity seems to generate a periodic attractor state that is hard to escape. Despite numerous studies, scientists and clinicians do not know exactly if ASD is a result of a simple yet general mechanism or of a complex set of mechanisms (either on the neural, molecular and system levels). Simulations using the biologically - relevant neural network model presented here may help to reveal the simplest mechanisms that may be responsible for specific behavior. Abnormal neural fatigue mechanisms may be responsible for motor symptoms as well as many (or perhaps all) of the other symptoms observed in ASD.

4

What Can a Mathematician do in Neuroscience?

Karbowski J.

Mathematica Applicanda

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2012

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Vol. 40, No. 1

27-37

EN

Mammalian brain is one of the most complex objects in the known universe, as it governs every aspect of animal’s and human behavior. It is fair to say that we have a very limited knowledge of how the brain operates and functions. Computational Neuroscience is a scientific discipline that attempts to understand and describe the brain in terms of mathematical modeling. This user-friendly review tries to introduce this relatively new field to mathematicians and physicists by showing examples of recent trends. It also discusses briefly future prospects for constructing an integrated theory of brain function.

PL

Mózg ssaków jest jednym z najbardziej złożonych obiektów we wszechświecie. Jest odpowiedzialny za sterowanie wszystkimi aspektami zachowań zwierzęcia i człowieka. Obecnie usprawiedliwione wydaje się stwierdzenie, ze nasza wiedza na temat pracy mózgu i jego funkcjach jest dość ograniczona. Neurobiologia obliczeniowa jest dyscyplina naukowa, która próbuje zrozumieć i opisać mózg w kategoriach modelowania matematycznego. W tej pracy zawarto przyjazny dla czytelnika przegląd zagadnień, który ma na celu wprowadzenie w ten stosunkowo nowy dla matematyków i fizyków obszar badawczy, pokazując przykłady najnowszych trendów w tej dziedzinie. Artykuł omawia także krótko przyszłe perspektywy dla budowy zintegrowanej teorii funkcji mózgu. Neurobiologia Obliczeniowa ma wiele osiągnięć w modelowaniu procesów neurofizjologicznych. W szczególności, realistyczne modelowanie dynamiki pojedynczych neuronów osiągnęło wysoki poziom wierności z danymi eksperymentalnymi. Wielkim wyzwaniem pozostaje natomiast kluczowe zagadnienie, jak przejść od opisu dynamiki pojedynczych neuronów do realistycznego opisu dynamiki całej sieci neuronów. Generalnie, poznanie i zrozumienie funkcjonowania mózgu w oparciu o modele matematyczne może mieć kolosalne znaczenie praktyczne dla społeczeństwa. Po pierwsze, w medycynie w radzeniu sobie z neurologicznymi schorzeniami takimi jak autyzm, schizofrenia, czy Alzheimer, które są coraz powszechniejsze. Mechanizmy biofizyczne tych chorób nie są znane, i być może dobra teoria funkcjonalna mogłaby w tym pomóc. Po drugie, w technologii tzw. inteligentnych urządzeń. Obecnie nawet najszybsze superkomputery nie są w stanie poradzić sobie z wydawało by się prostym zadaniem takim jak efektywne rozpoznawanie twarzy czy obiektów, z czym dość wolny ludzki mózg nie ma żadnych problemów. Bez wątpienia, inteligentne urządzenia skonstruowane na bazie mózgu miałyby bardzo wiele zastosowań, w różnych sferach działalności człowieka. Wydaje się, że zintegrowana teoria pracy mózgu mogłaby wiele wnieść w tym kierunku

5

Computational approach to understanding Autism Spectrum Disorders

Duch W., Nowak W., Meller J., Osiński G., Dobosz K., Mikołajewski D., Wójcik G. M.

Computer Science

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2012

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Vol. 13 (2)

47-61

EN

Every year the prevalence of Autism Spectrum of Disorders (ASD) is rising. Is there a unifying mechanism of various ASD cases at the genetic, molecular, cellular or systems level? The hypothesis advanced in this paper is focused on neural dysfunctions that lead to problems with attention in autistic people. Simulations of attractor neural networks performing cognitive functions help to assess system long-term neurodynamics. The Fuzzy Symbolic Dynamics (FSD) technique is used for the visualization of attractors in the semantic layer of the neural model of reading. Large-scale simulations of brain structures characterized by a high order of complexity requires enormous computational power, especially if biologically motivated neuron models are used to investigate the influence of cellular structure dysfunctions on the network dynamics. Such simulations have to be implemented on computer clusters in a grid-based architectures.